Motor Current Signature Analysis (MCSA) represents a well-known and widely used approach for monitoring the health state of motors and generators. Traditional implementations of MCSA involve the analysis of the frequency spectrum of a measured motor phase current in order to ascertain certain fault modes such as air-gap eccentricity, broken rotor bars and bearing damage. Torsional oscillations can also modulate the phase currents drawn by both synchronous and asynchronous motors resulting in sidebands in the current spectrum that, in theory, may be evaluated to determine the operating state of components connected to the motor. Such spectral analysis has been proposed for monitoring shaft fatigue cycles, load unbalances or shaft misalignments, or oscillations from a connected gearbox in both healthy and faulty conditions.
Traditional implementations of MCSA assume that the operating speed during the measurement period is constant. Often this assumption is valid, particularly in the most prevalent motor applications such as compressors, pumps or fans. However, there are a number of applications where the operating conditions of the motor are not constant. In particular grinding, chipping or pulping applications represent cases where the driving motors are subject to extremely variable loadings. In these applications the motor generally only operates at steady state, under virtually no-load conditions, where it can be difficult to diagnose faults due to the dependence of fault indicator amplitudes on loading. Not only are these applications difficult to analyze using standard methods, they also represent cases where the electrical machine is more likely to suffer failures due to the stresses caused by the non-stationary operating conditions.
From patent description EP 2523009 B1, a known method of monitoring the condition of electromechanical systems by synchronizing measured currents and/or voltages to an estimate of shaft angular position and averaging from rotation to rotation is given. Such a method is able to decouple current signatures relating to the mechanical part of the system from components relating to the electrical side, allowing them to be considered independently. Additionally, the act of linking the magnitude of a measured current to shaft angular positions renders the approach somewhat invariant to non-stationary operating conditions. In order to properly implement this approach, an accurate measure of the shaft speed or angular position is required. Traditionally, such information is recorded by a shaft mounted tachometer or encoder. Such sensors represent an additional cost, can be difficult to install and may be unreliable.
There are a wide range of methods for estimating the speed and torque of an electric motor using measurements of current and voltage. These approaches are often used in so-called ‘sensorless’ control strategies, which form the basis of many industrial drives. In “Sensorless vector and direct torque control” (Oxford University Press, U K, 1998, ISBN 978-0-19-856465-2), the techniques typically utilized to estimate the speed of a motor are given as open-loop estimators using the monitored stator voltages and currents, estimators using the spatial saturation stator phase third-harmonic voltage, estimators using saliency effects, model reference adaptive systems, observers such as Kalman filters, or Luenberger observers and estimators using Artificial Intelligence methods such as neural networks, fuzzy-logic or fuzzy-neural networks.
Of these categories, the method described in this invention may be most closely associated with the class of speed estimation techniques based on open-loop estimators using stator voltages and currents. The implementation of these open-loop approaches is relatively simple relative to other methods, however their success depends on the accuracy of the parameters in the model of the machine. Typical model parameters include stator resistance, rotor time constant, stator transient inductance and stator self-inductance. In a typical commercial drive the necessary parameters are often determined during an initial identification or self-commissioning run. However, model parameters may vary over the operation of the system due to, amongst others, temperature variations, skin effects and saturation effects. This motivates the use of model reference adaptive systems, observers, and artificial intelligence in control applications which require online estimates of rotor speed. From patent description U.S. Pat. No. 6,993,439 B2 there is a known method of estimating motor speed using currents and voltages for diagnostic purposes using Artificial Intelligence. Specifically, a neural network-based adaptive filter is described. Such approaches require a training period, during which model parameters are fitted to recorded signals.
From patent description U.S. Pat. No. 8,373,379 B2, a further known method of estimating motor speed using currents and voltages for diagnostic purposes is described. The described method may be classed as an estimator using saliency, specifically identifying how current components at the rotor slot harmonic frequency has been modulated by a varying motor speed. In order to apply such methods, knowledge of the machine construction is required, specifically the number of rotor slots. This information is not always readily available.